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19 pages, 8885 KiB  
Article
Slow-Release Nitrogen Fertilizer Promotes the Bacterial Diversity to Drive Soil Multifunctionality
by Tiantian Meng, Jingjing Shi, Xiangqian Zhang, Guolong Ge, Yuchen Cheng, Meiren Rong, Liyu Chen, Xiaoyu Zhao, Xiaoxiang Wang and Zhanyuan Lu
Agronomy 2024, 14(11), 2712; https://doi.org/10.3390/agronomy14112712 (registering DOI) - 17 Nov 2024
Abstract
The application of slow-release nitrogen fertilizer not only economizes labor input, but also decreases the frequency of use of mechanical intakes, with significant implications in advancing modern intensive agricultural production. Whether slow-release nitrogen fertilizer application can influence the association between microbial diversity and [...] Read more.
The application of slow-release nitrogen fertilizer not only economizes labor input, but also decreases the frequency of use of mechanical intakes, with significant implications in advancing modern intensive agricultural production. Whether slow-release nitrogen fertilizer application can influence the association between microbial diversity and soil multifunctionality remains controversial. This study analyzed the spatial variances of soil environmental factors, soil multifunctionality, and their correlations with bacterial and fungal communities under five nitrogen application rates. The key factors influencing the dominant microbial species and community structures at different spatial locations were determined by the slow-release nitrogen fertilizer application rate, and the driving factors and dominant species of soil multifunctionality were identified. In contrast to the control group, moderate slow-release nitrogen fertilizer application enhanced soil multifunctionality and ameliorated the resilience of microbial diversity loss at diverse spatial locations resulting from irrational nitrogen fertilizer application. The resilience of the fungal community to disturbances caused by fertilization was lower than that of the bacterial community. Bacterial diversity exhibited a significant correlation with soil multifunctionality, and the soil multifunctionality intensity under 240 kg ha−1 treatment increased by 159.01% compared to the CK. The main dominant bacterial communities and the dominant fungal community Ascomycota affected soil multifunctionality through slow-release nitrogen fertilizer application. Structural equation modeling and random forest analysis demonstrated that bacterial community diversity, particularly in bulk soil and the rhizosphere, community composition, and soil nitrogen form are the primary driving factors of soil multifunctionality. Results indicated that the microbial niche alterations induced by slow-release nitrogen fertilizer application positively affect soil multifunctionality. Full article
(This article belongs to the Section Soil and Plant Nutrition)
18 pages, 15070 KiB  
Article
Microbial Community of Wilted Fritillaria ussuriensis and Biocontrol Effects of Bacillus tequilensis and Trichoderma koningiopsis
by Hao Wu, Jingjing Lu, Simeng Zhao, Jingyi Fei, Zhimiao Qu, Min Zhao and Hongyan Yang
Biology 2024, 13(11), 940; https://doi.org/10.3390/biology13110940 (registering DOI) - 17 Nov 2024
Abstract
The cultivation of Fritillaria ussuriensis faces challenges due to the prevalent Fritillaria wilt disease, hindering large-scale production. To address this, we aimed to understand the disease’s characteristics and develop effective prevention measures. Microbial communities of diseased F. ussuriensis plants were analyzed, pathogenic and [...] Read more.
The cultivation of Fritillaria ussuriensis faces challenges due to the prevalent Fritillaria wilt disease, hindering large-scale production. To address this, we aimed to understand the disease’s characteristics and develop effective prevention measures. Microbial communities of diseased F. ussuriensis plants were analyzed, pathogenic and antagonistic strains were screened, and biocontrol feasibility was tested. We identified Botryotinia predominance in aboveground parts and variations in Mrakia, Humicola, llyonectria, and Fusarium in underground parts. The pathogens Fusarium oxysporum IFM-1 and Fusarium solani IFM-52 isolated from diseased F. ussuriensis not only caused severe Fritillaria wilt but were also pathogenic to Lilium lancifolium and Allium cepa var. aggregatum in Liliaceae. The antagonistic Bacillus tequilensis LFM-30 and Trichoderma koningiopsis IFM-47 isolated from diseased plants significantly alleviated plant wilt and showed promise in preventing wilt disease caused by Fusarium in Liliaceae plants. Our study highlights distinct microbial differences between healthy and diseased F. ussuriensis and underscores the pathogenicity of Fusarium. Using T. koningiopsis and B. tequilensis either singly or in combination could offer effective biocontrol against F. solani and F. oxysporum, benefiting F. ussuriensis and related Liliaceae plants. Full article
(This article belongs to the Section Plant Science)
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Figure 1

Figure 1
<p>Microbial richness and diversity in healthy and diseased plants and rhizosphere soils. (<b>A</b>,<b>C</b>) bacteria; (<b>B</b>,<b>D</b>) fungi. Different colors show differrent groups. HA: healthy aboveground tissue; HU: healthy underground tissue HR: healthy rhizosphere soil; DA: diseased aboveground tissue; DU: diseased underground tissue; DR: diseased rhizosphere soil. Values with different lowercase letters (a–e) above the bars indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The PCoA of differences in microbial composition between healthy and diseased plants and rhizosphere soil. (<b>A</b>) bacteria; (<b>B</b>) fungi. HA: healthy aboveground tissue; HU: healthy underground tissue HR: healthy rhizosphere soil; DA: diseased aboveground tissue; DU: diseased underground tissue; DR: diseased rhizosphere soil.</p>
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<p>Microbial community composition at phylum, family, and genus level in healthy and diseased groups. (<b>A</b>,<b>C</b>,<b>E</b>) bacteria; (<b>B</b>,<b>D</b>,<b>F</b>) fungi. HA: healthy aboveground tissue; HU: healthy underground tissue HR: healthy rhizosphere soil; DA: diseased aboveground tissue; DU: diseased underground tissue; DR: diseased rhizosphere soil.</p>
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<p>Difference analysis of community composition at genus level in healthy and diseased groups. (<b>A</b>,<b>C</b>,<b>E</b>) bacteria; (<b>B</b>,<b>D</b>,<b>F</b>) fungi. The red or blue colored circle represents the diseased or the healthy group, respectively. HA: healthy aboveground tissue; HU: healthy underground tissue HR: healthy rhizosphere soil; DA: diseased aboveground tissue; DU: diseased underground tissue; DR: diseased rhizosphere soil. The significant level is at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>The co-occurrence network analysis of microbial communities in different groups. The colored circles show different fungal or bacterial phyla.</p>
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<p>Screening of pathogens and antagonistic microorganisms. (<b>A</b>) Pathogens screening; (<b>B</b>) the inhibition rate based on plate confrontation. CK: no inoculation; IFM-1: <span class="html-italic">F. oxysporum</span>; IFM-52: <span class="html-italic">F. solani</span>; IFM-7~IFM-54: antagonistic fungi; LFM-4~LFM-38: antagonistic bacteria. Values with different lowercase letters (a–d) above the bars indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Phylogenetic analysis of LFM-30 16S rRNA (<b>A</b>) and gyrB (<b>B</b>) gene sequencing using the neighbor-joining method.</p>
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<p>Phylogenetic analysis of IFM-47 ITS (<b>A</b>) and β-tubulin (<b>B</b>) gene sequencing using the neighbor-joining method.</p>
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<p>Effects of in vitro <span class="html-italic">F. ussuriensis</span> bulb inoculation. CK: no inoculant; BT: <span class="html-italic">B. tequilensis</span> LFM-30; TK: <span class="html-italic">T. koningiopsis</span> IFM-47; KT: BT + TK; FO: <span class="html-italic">F. oxysporum</span> IFM-1; FOT: FO + BT; FOK: FO + TK; FOKT: FO + TK + BT; FS: <span class="html-italic">F. solani</span> IFM-52; FST: FS + BT; FSK: FS + TK; FSKT: FS + TK + BT.</p>
Full article ">Figure 10
<p>Inoculating effects of antagonistic microorganisms based on pot experiment. (<b>A</b>) <span class="html-italic">F. ussuriensis</span>; (<b>B</b>) <span class="html-italic">L. lancifolium</span>; (<b>C</b>) <span class="html-italic">A. cepa</span>. CK: no inoculant; BT: <span class="html-italic">B. tequilensis</span> LFM-30; TK: <span class="html-italic">T. koningiopsis</span> IFM-47; KT: BT + TK; FO: <span class="html-italic">F. oxysporum</span> IFM-1; FOT: FO + BT; FOK: FO + TK; FOKT: FO + TK + BT; FS: <span class="html-italic">F. solani</span> IFM-52; FST: FS + BT; FSK: FS + TK; FSKT: FS + TK + BT. (<b>D</b>) The disease severity index (DSI) of the three Liliaceae species. Values with different lowercase letters (a–c) above the bars indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Growth status of <span class="html-italic">F. ussuriensis</span> at the planting site while sampling. The left side of the blue dotted line shows the healthy field, and the right side shows the diseased field.</p>
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18 pages, 5263 KiB  
Article
Kiwifruit Vine Decline Syndrome (KVDS) Alters Soil Enzyme Activity and Microbial Community
by Valentino Bergamaschi, Alfonso Vera, Lucia Pirone, José A. Siles, Rubén López-Mondéjar, Laura Luongo, Salvatore Vitale, Massimo Reverberi, Alessandro Infantino and Felipe Bastida
Microorganisms 2024, 12(11), 2347; https://doi.org/10.3390/microorganisms12112347 (registering DOI) - 16 Nov 2024
Viewed by 432
Abstract
Kiwifruit Vine Decline Syndrome (KVDS) has become a major concern in Italy, impacting both plant health and production. This study aims to investigate how KVDS affects soil health indicators and the composition of soil microbial communities by comparing symptomatic and asymptomatic areas in [...] Read more.
Kiwifruit Vine Decline Syndrome (KVDS) has become a major concern in Italy, impacting both plant health and production. This study aims to investigate how KVDS affects soil health indicators and the composition of soil microbial communities by comparing symptomatic and asymptomatic areas in two kiwifruit orchards located in Latium, Italy. Soil samples were collected during both spring and autumn to assess seasonal variations in soil physicochemical properties, enzyme activities, and microbial biomass. The results reveal that KVDS influences several soil properties, including pH, electrical conductivity, and the contents of water-soluble carbon and nitrogen. However, these effects varied between orchards and across different seasons. Additionally, KVDS significantly impacts soil enzyme activities and microbial biomass, as assessed through the phospholipid fatty acid (PLFA) analysis, particularly showing an increase in fungal biomass in symptomatic areas. Metabarcoding further demonstrates that microbial communities differ between symptomatic and asymptomatic soils, exhibiting notable shifts in both diversity and relative abundance. Our findings emphasise the complex interactions between plants, soil, and microbial communities in relation to KVDS. This suggests that the syndrome is multifactorial and likely linked to an imbalance in soil microbial communities at the rhizosphere level, which can negatively affect soil health. Full article
(This article belongs to the Section Environmental Microbiology)
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Figure 1

Figure 1
<p>Sampling layout and orchard conditions. (<b>a</b>) Asymptomatic and (<b>b</b>) symptomatic kiwifruit orchard. Detailed view of kiwifruit trees with designated sampling points around the trunk for (<b>c</b>) asymptomatic and (<b>d</b>) symptomatic trees with the five specific sampling locations around each trunk.</p>
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<p>Scoring scale for disease severity (0–3) for epigeal and hypogeal symptoms in kiwifruit plants. Epigeal symptoms: (<b>b</b>) no symptoms (healthy plant); (<b>d</b>) mild symptoms (plant decline sometimes visible through reduced shoot growth, leaf chlorosis, fewer new shoots, and smaller leaves); (<b>f</b>) severe symptoms (leaf drop, fruit drop, reduced fruit size if present, and overall vine decline); (<b>h</b>) dead plant. Hypogeal symptoms: (<b>a</b>) no symptoms (healthy roots); (<b>c</b>) mild symptoms (reduction of absorbent roots and visible necrosis); (<b>e</b>) severe symptoms (decay of primary roots, near-complete rot of secondary roots, loss of cortical tissue, “rat-tail” appearance, and loss of absorbent roots); (<b>g</b>) dead roots. For root symptoms, images show roots during sampling (<b>left</b>) and after washing (<b>right</b>).</p>
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<p>Bar chart of soil physicochemical properties of Orchards 1 and 2 in Spring and Autumn. (<b>a</b>) pH, (<b>b</b>) SOC: soil organic carbon content, (<b>c</b>) WSC: soil water-soluble C, (<b>d</b>) EC: electrical conductivity, (<b>e</b>) TN: total soil nitrogen content, (<b>f</b>) WSN: soil water-soluble N, and the letters A and S indicate Asymptomatic and Symptomatic, respectively. Different letters (a, b) indicate significant differences based on One-Way ANOVA results at <span class="html-italic">p</span> &lt; 0.05. Error bars represent the standard error of the mean (4 replicates).</p>
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<p>Bar chart of β-glucosidase (μmol PNF g<sup>−1</sup> soil h<sup>−1</sup>) (<b>a</b>), alkaline phosphatase (μmol PNF g<sup>−1</sup> soil h<sup>−1</sup>) (<b>b</b>), urease (μmol NH<sub>4</sub><sup>+</sup> g<sup>−1</sup> soil h<sup>−1</sup>) (<b>c</b>), and basal soil respiration (BSR) (mg CO<sub>2</sub> kg<sup>−1</sup> soil day<sup>−1</sup>) (<b>d</b>) of Orchard 1 and 2 in Spring and Autumn. The letters A and S indicate Asymptomatic and Symptomatic, respectively. Different letters (a, b) indicate significant differences based on One-Way ANOVA results at <span class="html-italic">p</span> &lt; 0.05. Error bars represent the standard error of the mean (4 replicates).</p>
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<p>Bar chart of biomass abundance (nmol g<sup>−1</sup> soil h<sup>−1</sup>) divided into Bacteria (<b>a</b>), Fungi (<b>b</b>), Gram- Total Biomass (<b>c</b>), Fungi/Bacteria ratio (<b>d</b>), of Orchard 1 and 2 in Spring and Autumn. Different letters (a, b) indicate significant differences based on One-Way ANOVA results at <span class="html-italic">p</span> &lt; 0.05. Error bars represent the standard error of the mean (4 replicates).</p>
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<p>Heat map of Spearman’s correlation for soil and physiological parameters. Negative and positive correlations are represented in blue and red, respectively. All correlations are significant at <span class="html-italic">p</span> &lt; 0.05. EC: electrical conductivity, WSC: soil water-soluble C, WSN: soil water-soluble N, Amm: soil ammonium content, Ntotal: total soil nitrogen content, C total: total soil carbon content, SOC: soil organic carbon, CaCO<sub>3</sub>: soil carbonate calcium content, CN: carbon/nitrogen ratio, bG: β-glucosidase activity, alkP: alkaline phosphatase activity, Ure: urease activity, BSR: basal soil respiration, Fun: soil fungal biomass, Bac: soil bacterial biomass, TB: total soil microbial biomass.</p>
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<p>Composition of fungal (<b>a</b>) and bacterial (<b>b</b>) abundance at phylum level in Orchards 1 and 2 in Spring and Autumn.</p>
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<p>NMDS analysis of fungal community (<b>a</b>) and bacterial community (<b>b</b>) from Orchard 1 in Spring (dot) and Autumn (triangle), and Orchard 2 in Spring (square) and Autumn (cross). The asymptomatic and symptomatic samples are shown in red and blue, respectively. For the PerMANOVA test, the significance levels are shown at * <span class="html-italic">p</span> ≤ 0.05, and *** <span class="html-italic">p</span> ≤ 0.001. O×C means orchard and status interaction.</p>
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19 pages, 6417 KiB  
Article
Effects of Rice Root Development and Rhizosphere Soil on Methane Emission in Paddy Fields
by Sheng Guan, Zhijuan Qi, Sirui Li, Sicheng Du and Dan Xu
Plants 2024, 13(22), 3223; https://doi.org/10.3390/plants13223223 (registering DOI) - 16 Nov 2024
Viewed by 161
Abstract
Paddy fields are important anthropogenic emission sources of methane (CH4). However, it is not clear how rice root development and rhizosphere soil properties affect CH4 emissions. Therefore, we selected rice varieties with similar growth periods but different root traits in [...] Read more.
Paddy fields are important anthropogenic emission sources of methane (CH4). However, it is not clear how rice root development and rhizosphere soil properties affect CH4 emissions. Therefore, we selected rice varieties with similar growth periods but different root traits in the local area. We measured CH4 emission fluxes, cumulative CH4 emissions, root dry weight, root length, and the dissolved organic carbon (DOC), microbial biomass carbon (MBC), redox potential (Eh), ammonium nitrogen (NH+ 4–N), and nitrate nitrogen (NO− 3–N) contents in rhizosphere soil. Methanogens and methanotrophs are crucial factors influencing CH4 emissions; thus, their abundance and community composition were also assessed. The result showed that CH4 fluxes of each rice variety reached the peak at tillering stage and jointing-booting stage. The CH4 emissions in tillering stage were the largest in each growth period. CH4 emissions had negative correlations with root length, root dry weight, Eh NO− 3–N, methanotroph abundance, and the pmoA/mcrA ratio, and positive correlations with NH+ 4–N, MBC, DOC, and methanogen abundance. Path analysis confirmed methanogens and methanotrophs as direct influences on CH4 emissions. Root development and rhizosphere soil properties affect CH4 emissions indirectly through these microbes. This study suggests that choosing rice varieties with good root systems and managing the rhizosphere soil can effectively reduce CH4 emissions. Full article
(This article belongs to the Special Issue Plant Root: Anatomy, Structure and Development)
25 pages, 4860 KiB  
Article
Changes in the Gut and Oral Microbiome in Children with Phenylketonuria in the Context of Dietary Restrictions—A Preliminary Study
by Malgorzata Ostrowska, Karolina Nowosad, Bozena Mikoluc, Hubert Szczerba and Elwira Komon-Janczara
Nutrients 2024, 16(22), 3915; https://doi.org/10.3390/nu16223915 (registering DOI) - 16 Nov 2024
Viewed by 375
Abstract
Background: Phenylketonuria (PKU) is a metabolic disorder that necessitates dietary restrictions, potentially impacting the composition of gut and oral microbiota. This study aimed to compare the microbiota composition between children with PKU and healthy controls. Methods: Using 16S rRNA gene sequencing, we analysed [...] Read more.
Background: Phenylketonuria (PKU) is a metabolic disorder that necessitates dietary restrictions, potentially impacting the composition of gut and oral microbiota. This study aimed to compare the microbiota composition between children with PKU and healthy controls. Methods: Using 16S rRNA gene sequencing, we analysed microbial communities at six phylogenetic levels. Results: Our findings revealed significant differences in the gut microbiota: Euryarchaeota was more abundant in controls (p = 0.01), while Bacilli and Lactobacillales were higher in PKU children (p = 0.019). Methanobacteriales were significantly elevated in controls (p = 0.01). At the genus and species levels, PKU children had higher Streptococcus and Eubacterium dolichum (p = 0.019, p = 0.015), whereas controls had more Barnesiella, Coprococcus, and Faecalibacterium prausnitzii (p = 0.014, p = 0.019, p = 0.014). In the oral microbiota, control children exhibited significantly higher Bacteroidetes (p = 0.032), while PKU children had increased Bacilli and Betaproteobacteria (p = 0.0079, p = 0.016). Streptococcus and Neisseria were more prevalent in PKU (p = 0.0079, p = 0.016). Conclusions: These results suggest that PKU and its dietary management significantly alter the gut and oral microbiota composition. Understanding these microbial shifts could have implications for managing PKU and improving patient outcomes. Full article
(This article belongs to the Section Nutrition and Public Health)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>A</b>–<b>F</b>) Comparison of gut microbiota composition between PKU and healthy control children. The bar plots represent the mean relative abundance of the six taxonomic levels: (<b>A</b>) phylum, (<b>B</b>) class, (<b>C</b>) order, (<b>D</b>) family, (<b>E</b>) genus, and (<b>F</b>) species (&gt;0.01 mean relative abundance). The asterisk indicates a significant correlation with a * <span class="html-italic">p</span>-value &lt; 0.05.</p>
Full article ">Figure 2
<p>Heatmap of gut microbiota abundance in the control and PKU groups. Group of children with phenylketonuria (PKU1, PKU2, PKU3, PKU4, and PKU5) compared to the control children (C1, C2, C3, and C4) group. Each row represents a different bacterial genus or species. The colour gradient indicates levels of abundance, with dark blue indicating very little or no presence and red indicating the most abundant. The colour bar above the heatmap shows the treatment status for control and PKU children.</p>
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<p>Gut microbiome bacterial taxonomic predominance and mean abundance in PKU. The scatterplot showed the relationship between the prevalence and mean abundance of various bacterial taxa in the gut microbiome, with both axes on a logarithmic scale. The <span class="html-italic">x</span>-axis represents the prevalence (log10), and the <span class="html-italic">y</span>-axis represents the mean abundance (log10). Each point represents a different bacterial taxon, colour-coded by phylum. The error bars indicate variability in abundance and prevalence. The dashed line represents a threshold separating taxa with a mean abundance higher than 0.010 log 10.</p>
Full article ">Figure 4
<p>(<b>A</b>–<b>F</b>) Comparison of oral microbiota composition between PKU and healthy control children. The bar plots represent the mean relative abundance of the six taxonomic levels: (<b>A</b>) phylum, (<b>B</b>) class, (<b>C</b>) order, (<b>D</b>) family, (<b>E</b>) genus, and (<b>F</b>) species (&gt;0.01 mean relative abundance). The asterisk indicates significant correlation. * indicates <span class="html-italic">p</span>-value &lt; 0.05.</p>
Full article ">Figure 5
<p>Heatmap of oral microbiota abundance in the control and PKU groups. Group of children with phenylketonuria (PKU1, PKU2, PKU3, PKU4, and PKU5) compared to the group of control children (C1, C2, C3, C4, and C5). Each row represents a different bacterial genus or species, identified by unique codes followed by names. The colour gradient indicates levels of abundance, with dark blue indicating very little or no presence and red indicating the most abundant. The colour bar above the heat map shows the treatment status for control and PKU children.</p>
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<p>Oral microbiome bacterial taxonomic predominance and mean abundance in PKU. The scatterplot shows the relationship between the prevalence and mean abundance of various bacterial taxa in the oral microbiome, with both axes on a logarithmic scale. The <span class="html-italic">x</span>-axis represents the prevalence (log10), and the <span class="html-italic">y</span>-axis represents the mean abundance (log10). Each point represents a different bacterial taxon, colour-coded by phylum. The error bars indicate variability in abundance and prevalence. The dashed line at 10<sup>−2</sup> x log(10) represents a threshold separating taxa with a higher mean abundance, considered potentially more biologically significant.</p>
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<p>(<b>A</b>,<b>B</b>) Alpha diversity of taxa was identified in the gut microbiota between the control group and individuals with PKU (<b>A</b>), as well as in the oral microbiota between the control group and those with PKU (<b>B</b>). Alpha diversity analysis was conducted using many metrics to evaluate the variety within each sample and compare the number of species across different conditions being studied. Observed, Shannon, and Simpson diversity indices were estimated. The Wilcoxon rank-sum test was used to identify statistically significant differences. (*) <span class="html-italic">p</span> &lt; 0.05 indicates a significant difference, whereas (ns) <span class="html-italic">p</span> &lt; 0.05 indicates no significant difference.</p>
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<p>Differential taxon abundance analysis between gut PKU and control samples. The volcano plot illustrates the results of a differential microbial composition analysis comparing gut samples from the PKU group to the control group using the ALDEx2 method. The <span class="html-italic">x</span>-axis represents the log2 fold change in microbiota compositions between the two groups, with positive values indicating higher expression in the PKU group and negative values indicating higher expression in the control group. The <span class="html-italic">y</span>-axis represents the -log10 transformed <span class="html-italic">p</span>-values, with higher values indicating greater statistical significance. Each point on the plot corresponds to a single taxon. The red dots represent taxa that reached statistical significance with a higher level of confidence, often implying that these points have surpassed a certain threshold for significance. The dark dots indicate taxa that did not show statistical significance. The dashed line marks the significance threshold at <span class="html-italic">p</span> = 0.1 (-log10(0.1)).</p>
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18 pages, 5279 KiB  
Article
Enhanced Oil Recovery in a Co-Culture System of Pseudomonas aeruginosa and Bacillus subtilis
by Dingyu Kang, Hai Lin, Qiang Li, Nan Su, Changkun Cheng, Yijing Luo, Zhongzhi Zhang and Zhiyong Zhang
Microorganisms 2024, 12(11), 2343; https://doi.org/10.3390/microorganisms12112343 (registering DOI) - 16 Nov 2024
Viewed by 239
Abstract
Microbial enhanced oil recovery (MEOR) is a promising technology for oil field extraction. This study investigated a co-culture system of Pseudomonas aeruginosa and Bacillus subtilis to increase MEOR efficacy. We analyzed bacterial growth, biosurfactant production, and crude oil emulsified performance under different inoculation [...] Read more.
Microbial enhanced oil recovery (MEOR) is a promising technology for oil field extraction. This study investigated a co-culture system of Pseudomonas aeruginosa and Bacillus subtilis to increase MEOR efficacy. We analyzed bacterial growth, biosurfactant production, and crude oil emulsified performance under different inoculation ratios. Compared to single cultures, the co-culture system showed superior growth and functional expression, with an optimal inoculation ratio of 1:1. Quantitative assessments of the cell numbers and biosurfactant production during the co-culture revealed that rapid B. subtilis proliferation in early stages significantly stimulated P. aeruginosa growth. This interaction increased cell density and rhamnolipid production by 208.05% and 216.25%, respectively. The microscopic etching model displacement results demonstrated enhanced emulsification and mobilization of crude oil by the co-culture system, resulting in 94.48% recovery. A successful field application in a block-scale reservoir increased cumulative oil production by 3.25 × 103 t. An analysis of microbial community structure and function in different phases revealed that after co-culture system injection, Pseudomonas became the dominant genus in the reservoir community, with an average abundance of 24.80%. Additionally, the abundance of biosurfactant-producing and hydrocarbon-degrading bacteria increased significantly. This research and the application of the P. aeruginosa and B. subtilis co-culture system provide novel insights and strategies for MEOR. Full article
(This article belongs to the Special Issue Advances in Microbial Surfactants: Production and Applications)
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Figure 1
<p>The single-culture and co-culture systems and the determination method of colony-forming unit.</p>
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<p>(<b>a</b>) The OD600 values; (<b>b</b>) Surface tension; (<b>c</b>) Oil spreading diameter: (<b>d</b>) Emulsification index in culture systems with different inoculum ratios. Letters on the graph indicate significant differences between the data.</p>
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<p>(<b>a</b>) Cell numbers of <span class="html-italic">P. aeruginosa</span> and <span class="html-italic">B. subtilis</span>; (<b>b</b>) Rhamnolipid and surfactin production in single culture and co-culture. ***, <span class="html-italic">p</span> &lt; 0.001; **, <span class="html-italic">p</span> &lt; 0.01; *, <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Characteristics of residual oil distribution in different regions in the microscopic etching model. (<b>a1</b>–<b>a4</b>) Saturated oil; (<b>b1</b>–<b>b4</b>) Water flooding; (<b>c1</b>–<b>c4</b>) <span class="html-italic">P. aeruginosa</span> flooding; (<b>d1</b>–<b>d4</b>) <span class="html-italic">B. subtilis</span> flooding; (<b>e1</b>–<b>e4</b>) Co-culture of P and B.</p>
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<p>Emulsion following various fluid displacements. (<b>a</b>) Microscopic image of emulsified oil droplets; (<b>b</b>) Particle size distribution of the oil droplets. The red curve is the size composition distribution curve.</p>
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<p>Production performance and oil production increase rates in the field trial. The arrows represent sampling dates. Gray coding indicates the pre-injection phase, red coding indicates the injection phase, and green coding indicates the post-injection phase. The pre-injection, injection, and post-injection phases together constitute the complete MEOR experimental cycle.</p>
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<p>The relative abundances of bacterial orders in the representative oil well during different MEOR phases. Only the top 10 orders are visualized.</p>
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<p>(<b>a</b>) The potential functions were predicted by FAPRTAX. (<b>b</b>) Functional genes were predicted by PICRUSt2. The data were normalized before visualization.</p>
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24 pages, 7266 KiB  
Article
Cast from the Past? Microbial Diversity of a Neolithic Stone Circle
by Mercedes Martín-Cereceda, Amaya de Cos-Gandoy, Richard A. J. Williams, David Elliott, Andrea Serrano-Bellón, Blanca Pérez-Uz and Abel Sanchez-Jimenez
Microorganisms 2024, 12(11), 2338; https://doi.org/10.3390/microorganisms12112338 (registering DOI) - 16 Nov 2024
Viewed by 354
Abstract
We studied the microbial diversity colonizing limestone rock pools at a Neolithic Monument (Arbor Low, Derbyshire, England). Five pools were analyzed: four located at the megaliths of the stone circle and one pool placed at the megalith at the Gib Hill burial mound [...] Read more.
We studied the microbial diversity colonizing limestone rock pools at a Neolithic Monument (Arbor Low, Derbyshire, England). Five pools were analyzed: four located at the megaliths of the stone circle and one pool placed at the megalith at the Gib Hill burial mound 300 m distant. Samples were taken from rock pool walls and sediments, and investigated through molecular metabarcoding. The microbiome consisted of 23 phyla of bacteria (831 OTUs), 4 phyla of archaea (19 OTUs), and 27 phyla of microbial eukarya (596 OTUs). For bacteria, there were statistically significant differences in wall versus sediment populations, but not between pools. For archaea and eukarya, significant differences were found only between pools. The most abundant bacterial phylum in walls was Cyanobacteriota, and Pseudomonadota in sediments. For archaea and microbial eukarya, the dominant phyla were Euryarcheota and Chlorophyta, respectively, in both wall and sediments. The distant pool (P5) showed a markedly different community structure in phyla and species, habitat discrimination, and CHN content. Species sorting and dispersal limitation are discussed as mechanisms structuring the microbiome assemblages and their spatial connectivity. The Arbor Low microbiome is composed of terrestrial representatives common in extreme environments. The high presence of Cyanobacteriota and Chlorophyta in the Arbor Low stones is troubling, as these microorganisms can induce mechanical disruption by penetrating the limestone matrix through endolithic/chasmoendolithic growth. Future research should focus on the metabolic traits of strains to ascertain their implication in bioweathering and/or biomineralization. Full article
(This article belongs to the Section Environmental Microbiology)
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<p>Arbor Low monument in the Derbyshire Peak District National Park (England, UK). (<b>A</b>) Stone circle and Gib Hill Barrow; P5: Pool 5 located at Gib Hill; (<b>B</b>) Location of pools P1 to P4 in the stone circle image adapted from Google Maps. Imagery © 2024 Google, Maxar Technologies, Map Data ©2024.</p>
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<p>Rock pools sampled in Arbor Low. Arrow shows the e-button data logger in P3 (see Materials and Methods for more details on the e-buttons). P1: Pool 1; P2: Pool 2; P3: Pool 3; P4: Pool 4; P5: Pool 5.</p>
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<p>Location of soil sample. (<b>A</b>) Arbor Low central cove of stones. Arrow points to the stone where the soil sample (P0) was taken. (<b>B</b>) Detail of the area where P0 was collected. Arrow points to the e-button logger at P0 sampling site.</p>
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<p>Venn diagrams showing the exclusive and shared OTUs retrieved in the five pools (P1 to P5). (<b>A</b>): Bacteria; (<b>B</b>): archaea; (<b>C</b>): eukarya.</p>
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<p>Relative abundance (%) of bacterial phyla in the wall and sediment (sed) of Arbor Low pools. Only phyla with a relative abundance higher than 1% are represented.</p>
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<p>Relative abundance (%) of eukaryotic phyla in the wall and sediment (sed) of Arbor Low pools. Only phyla with a relative abundance higher than 1% are represented.</p>
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<p>Box plots for OTU richness, Shannon, and inverse Simpson diversity indices in the overall pool wall and sediment (sed). (<b>A</b>): Bacteria; (<b>B</b>): archaea; (<b>C</b>): eukarya. Boxes represent the interquartile range and thick lines are the median. Whiskers indicate the highest and lowest values.</p>
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<p>OTU richness, Shannon, and inverse Simpson diversity indices in wall and sediment (sed) per pool. (<b>A</b>): Bacteria; (<b>B</b>): archaea; (<b>C</b>): eukarya.</p>
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<p>Normalized rarefaction curves of the number of OTUs in the soil, wall, and sediment (sed) samples. (<b>A</b>): Bacteria; (<b>B</b>): archaea; (<b>C</b>): eukarya.</p>
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<p>Bray–Curtis-based non-metric multidimensional scaling (NMDS) plot for the total OTUs of bacteria (<b>A</b>), archaea (<b>B</b>), and eukarya (<b>C</b>) in the wall and sediment (sed) samples.</p>
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<p>Hierarchical cluster of bacteria (<b>A</b>), archaea (<b>B</b>), and eukarya (<b>C</b>) per pool (P1 to P5) and type of sample: wall or sediment (sed). P0 represents the soil sample.</p>
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<p>Redundancy analysis (RDA) for the OTUs of archaea (response variables) and the CHN sediment values (explanatory variables). All archaeal OTUs were included as ISA did not retrieve pool indicator species.</p>
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17 pages, 8788 KiB  
Article
Effects of Deep Tillage on Rhizosphere Soil and Microorganisms During Wheat Cultivation
by Junkang Sui, Chenyu Wang, Feifan Hou, Xueting Shang, Qiqi Zhao, Yuxuan Zhang, Yongqiang Hou, Xuewen Hua and Pengfei Chu
Microorganisms 2024, 12(11), 2339; https://doi.org/10.3390/microorganisms12112339 (registering DOI) - 16 Nov 2024
Viewed by 319
Abstract
The production of wheat is fundamentally interconnected with worldwide food security. The practice of deep tillage (DT) cultivation has shown advantages in terms of soil enhancement and the mitigation of diseases and weed abundance. Nevertheless, the specific mechanisms behind these advantages are unclear. [...] Read more.
The production of wheat is fundamentally interconnected with worldwide food security. The practice of deep tillage (DT) cultivation has shown advantages in terms of soil enhancement and the mitigation of diseases and weed abundance. Nevertheless, the specific mechanisms behind these advantages are unclear. Accordingly, we aimed to clarify the influence of DT on rhizosphere soil (RS) microbial communities and its possible contribution to the improvement of soil quality. Soil fertility was evaluated by analyzing several soil characteristics. High-throughput sequencing techniques were utilized to explore the structure and function of rhizosphere microbial communities. Despite lowered fertility levels in the 0–20 cm DT soil layer, significant variations were noted in the microbial composition of the DT wheat rhizosphere, with Acidobacteria and Proteobacteria being the most prominent. Furthermore, the abundance of Bradyrhizobacteria, a nitrogen-fixing bacteria within the Proteobacteria phylum, was significantly increased. A significant increase in glycoside hydrolases within the DT group was observed, in addition to higher abundances of amino acid and carbohydrate metabolism genes in the COG and KEGG databases. Moreover, DT can enhance soil quality and boost crop productivity by modulating soil microorganisms’ carbon and nitrogen fixation capacities. Full article
(This article belongs to the Special Issue Advances in Soil Microbial Ecology, 2nd Edition)
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<p>The α-diversities of deep-tillage cultivation group’s and control group’s wheat rhizosphere soil microbes. (<b>a</b>) for Chao index value, (<b>b</b>) for Simpson index value, (<b>c</b>) for shannon index value.DT and CK: Deep- and non-deep-tillage cultivated wheat rhizosphere soil groups, respectively.</p>
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<p>β-diversities (NMDS and ANOSIM analysis) of DT and CK cultivation wheat rhizosphere soil microbes. (<b>a</b>) for NMDS analysis, (<b>b</b>) for ANOSIM analysis. DT and CK: Deep- and non-deep-tillage cultivated wheat rhizosphere soil groups, respectively.</p>
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<p>Phylum-level compositions (<b>a</b>) and differences (<b>b</b>) and genus-level compositions (<b>c</b>) and differences (<b>d</b>) in the rhizosphere soil (RS) microbiome in various cultivation modes. * represented 0.01 &lt; <span class="html-italic">p</span> ≤ 0.05, ** represented 0.001 &lt; <span class="html-italic">p</span> ≤ 0.01, *** represented <span class="html-italic">p</span> ≤ 0.001. DT and CK: Deep- and non-deep-tillage cultivated wheat RS groups, respectively.</p>
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<p>Gene abundance in microbes of deep- and non-deep-tillage rhizosphere soil. (<b>a</b>) Relative abundance changes in COG genes; (<b>b</b>) KEGG metabolic pathway-related functional genes.</p>
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<p>Carbohydrate enzyme-related genes in DT and CK rhizosphere soil (RS) microbes. (<b>a</b>) Proportion of the carbohydrate enzyme-correlated genes in the RS of both groups; (<b>b</b>) comparison of the difference in carbohydrate enzyme-correlated genes in the RS microbes of both groups. ** represented 0.001 &lt; <span class="html-italic">p</span> ≤ 0.01. DT and CK: Deep- and non-deep-tillage cultivated wheat RS groups, respectively.</p>
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<p>Antibiotic resistance ontology (ARO) composition and abundance. (<b>a</b>) ARO abundance in the rhizosphere soil of deep-tillage cultivated wheat; (<b>b</b>) ARO composition and (<b>c</b>) difference in both groups. * represented 0.01 &lt; <span class="html-italic">p</span> ≤ 0.05, ** represented 0.001 &lt; <span class="html-italic">p</span> ≤ 0.01, *** represented <span class="html-italic">p</span> ≤ 0.001. DT and CK: Deep- and non-deep-tillage cultivated wheat RS groups, respectively.</p>
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18 pages, 4511 KiB  
Article
Spatial Variability of Soil CO2 Emissions and Microbial Communities in a Mediterranean Holm Oak Forest
by Claudia Di Bene, Loredana Canfora, Melania Migliore, Rosa Francaviglia and Roberta Farina
Forests 2024, 15(11), 2018; https://doi.org/10.3390/f15112018 (registering DOI) - 15 Nov 2024
Viewed by 234
Abstract
Forests play a key role in the global carbon (C) cycle through multiple interactions between above-ground and soil microbial communities. Deeper insights into the soil microbial composition and diversity at different spatial scales and soil depths are of paramount importance. We hypothesized that [...] Read more.
Forests play a key role in the global carbon (C) cycle through multiple interactions between above-ground and soil microbial communities. Deeper insights into the soil microbial composition and diversity at different spatial scales and soil depths are of paramount importance. We hypothesized that in a homogeneous above-ground tree cover, the heterogeneous distribution of soil microbial functional diversity and processes at the small scale is correlated with the soil’s chemical properties. From this perspective, in a typical Mediterranean holm oak (Quercus ilex L.) peri-urban forest, soil carbon dioxide (CO2) emissions were measured with soil chambers in three different plots. In each plot, to test the linkage between above-ground and below-ground communities, soil was randomly sampled along six vertical transects (0–100 cm) to investigate soil physico-chemical parameters; microbial processes, measured using Barometric Process Separation (BaPS); and structural and functional diversity, assessed using T-RFLP and qPCR Real Time analyses. The results highlighted that the high spatial variability of CO2 emissions—confirmed by the BaPS analysis—was associated with the microbial communities’ abundance (dominated by bacteria) and structural diversity (decreasing with soil depth), measured by H′ index. Bacteria showed higher variability than fungi and archaea at all depths examined. Such an insight showed the clear ecological and environmental implications of soil in the overall sustainability of the peri-urban forest system. Full article
(This article belongs to the Special Issue Soil Organic Carbon and Nutrient Cycling in the Forest Ecosystems)
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<p>Daily mean air temperature monitored in the 0–24 h (°C) (shown as black continuous line), daily mean air temperature monitored at the time of soil CO<sub>2</sub> emissions measurements between 10 and 14 h (°C) (shown as black dotted line), and daily total rainfall (mm) (shown as gray column) over the soil CO<sub>2</sub> emissions monitoring period at the Castelporziano Reserve (Rome, Italy). A period was considered “dry” when the rainfall was equal to or less than twice the mean temperature (<b>a</b>). Daily mean soil temperature (°C) (shown as black dashed line) and daily mean soil water content (%, <span class="html-italic">v</span>:<span class="html-italic">v</span>) (shown as black continuous line) measured at 10 cm and 100 cm soil depth, respectively, over the soil CO<sub>2</sub> emissions monitoring period. Black and white circles represent daily mean soil temperature and daily mean water content, respectively, monitored at the time of soil CO<sub>2</sub> emissions measurements between 10 and 14 h (°C). (<b>b</b>) Soil CO<sub>2</sub> emissions (µmol CO<sub>2</sub> m<sup>−2</sup> s<sup>−1</sup>) were measured at the site in three plots (shown as plot 1: black triangle plot 2: white circle and plot 3: white diamond) during the period 6 June to 20 November 2013 with weekly or monthly soil CO<sub>2</sub> emissions monitoring (<span class="html-italic">n</span> = 12). Values are means ± SE (showed as vertical bars) of three replicates for each plot. For each measuring date, statistically significant differences among plots are shown by asterisks according to ANOVA (<span class="html-italic">* p</span> &lt; 0.05) (<b>c</b>).</p>
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<p>BaPS parameters measured at 0–20 cm layer: soil respiration rate (<span class="html-italic">RS</span>; mg C kg<sup>−1</sup> h<sup>−1</sup>) (<b>a</b>), gross denitrification rate (<span class="html-italic">Denitr</span>; µg N kg<sup>−1</sup> h<sup>−1</sup>) (<b>b</b>), and gross nitrification rate (<span class="html-italic">Nitr</span>; µg N kg<sup>−1</sup> h<sup>−1</sup>) (<b>c</b>). Values are means ± SE (showed as vertical bars) of three replicates for each plot. <span class="html-italic">Denitr</span> rate was detected in plots 1–2, while <span class="html-italic">Nitr</span> rate was only detected in plot 3. Values not followed by the same small letter are significantly different among plots within the same soil depth, according to ANOVA (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>BaPS parameters measured at 0–20 cm layer: soil respiration rate (<span class="html-italic">RS</span>; mg C kg<sup>−1</sup> h<sup>−1</sup>) (<b>a</b>), gross denitrification rate (<span class="html-italic">Denitr</span>; µg N kg<sup>−1</sup> h<sup>−1</sup>) (<b>b</b>), and gross nitrification rate (<span class="html-italic">Nitr</span>; µg N kg<sup>−1</sup> h<sup>−1</sup>) (<b>c</b>). Values are means ± SE (showed as vertical bars) of three replicates for each plot. <span class="html-italic">Denitr</span> rate was detected in plots 1–2, while <span class="html-italic">Nitr</span> rate was only detected in plot 3. Values not followed by the same small letter are significantly different among plots within the same soil depth, according to ANOVA (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Abundance of bacteria, archaea, and fungi expressed as gene copy numbers (g<sup>−1</sup> of soil dry weight) as detected along the investigated soil depth profile (0–100 cm) in each plot (bacteria as blue line and dots, archaea as red line and dots, and fungi as green line and dots). The abundance of each microbial community represents the average value of duplicate quantifications using 16S rDNA q-PCR analysis. Gene copy numbers were expressed in scientific notation. 0E+00 and 6E+13 refer to numbers ranging from 5.83 × 10<sup>8</sup> to 5.40 × 10<sup>13</sup>.</p>
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<p>Vertical changes in numbers of bacteria, archaea, and fungi phylotypes detected along the investigated soil depth in each plot (bacteria as blue dots, archaea as red squares, and fungi as green triangles). The number of phylotypes corresponds to the number of bands on the T-RFLP profiles.</p>
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<p>Dendrograms show similarity of T-RFLP profiles using Bray–Curtis hierarchical cluster analysis along the investigated soil depth in each plot.</p>
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<p>Boxplots of diversity index (Shannon index; <span class="html-italic">H′</span>). Three different soil layers (i.e., SL, superficial layer; IL, intermediate layer; and DL, deeper layer) were discriminated according to an arbitrary analysis of soil profile. Diversity was calculated from the number and the relative peak area of bands on the T-RFLP profiles.</p>
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<p>Principal component analysis (PCA) biplot was based on soil chemical and physical parameters (pH; SOC: soil organic carbon concentration; TN: total nitrogen concentration; C/N ratio; SWC: soil water content; Soil Temp: soil temperature), microbial processes <span class="html-italic">(Rs</span>: soil respiration; <span class="html-italic">Denitr</span>: denitrification rate; <span class="html-italic">Nitr</span>: gross nitrification rate), soil CO<sub>2</sub> emissions (measured using survey soil respiration chamber), microbial abundance (<span class="html-italic">Arch</span>: Archaea abundance; <span class="html-italic">Bact</span>: bacteria abundance; <span class="html-italic">Fungi</span>: fungi abundance) and Shannon index (Arch <span class="html-italic">H′</span>: Archaea Shannon index; Bact <span class="html-italic">H′</span>: Bacteria Shannon index; Fungi <span class="html-italic">H′</span>: Fungi Shannon index). All such parameters were used as variables, while replicate plots were used as observations in the 0–20 cm soil depth. Soil chemical and physical variables are shown by black continuous arrows, microbial processes and soil CO<sub>2</sub> emissions are shown by grey heavy dotted arrows, microbial <span class="html-italic">H’</span> is shown by black heavy dotted arrows, and microbial abundance is shown by light dotted arrows. Observations are represented by black stars (plot 1), white triangles (plot 2), and white squares (plot 3). PC 1 and PC 2 axes together accounted for 77.78% of the variability and were significant (<span class="html-italic">p</span> &lt; 0.05) (<b>a</b>). PCA biplot was based on soil chemical parameters, microbial abundance, and <span class="html-italic">H’</span>, which were used as variables, while soil depths were considered as observations. Observations (10: 0–10 cm, 20:10–20 cm, 40: 20–40 cm, 60: 40–60 cm, 80: 60–80 cm, 100: 80–100 cm) are represented by black circles. In plot 1, the PC 1 and PC 2 axes together accounted for 76.04% of the variability and were significant (<span class="html-italic">p</span> &lt; 0.05) (<b>b</b>); in plot 2, the PC 1 and PC 2 axes together accounted for 81.22% of the variability and were significant (<span class="html-italic">p</span> &lt; 0.05) (<b>c</b>); in plot 3, the PC 1 and PC 2 axes together accounted for 82.84% of the variability and were significant (<span class="html-italic">p</span> &lt; 0.05) (<b>d</b>).</p>
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<p>Principal component analysis (PCA) biplot was based on soil chemical and physical parameters (pH; SOC: soil organic carbon concentration; TN: total nitrogen concentration; C/N ratio; SWC: soil water content; Soil Temp: soil temperature), microbial processes <span class="html-italic">(Rs</span>: soil respiration; <span class="html-italic">Denitr</span>: denitrification rate; <span class="html-italic">Nitr</span>: gross nitrification rate), soil CO<sub>2</sub> emissions (measured using survey soil respiration chamber), microbial abundance (<span class="html-italic">Arch</span>: Archaea abundance; <span class="html-italic">Bact</span>: bacteria abundance; <span class="html-italic">Fungi</span>: fungi abundance) and Shannon index (Arch <span class="html-italic">H′</span>: Archaea Shannon index; Bact <span class="html-italic">H′</span>: Bacteria Shannon index; Fungi <span class="html-italic">H′</span>: Fungi Shannon index). All such parameters were used as variables, while replicate plots were used as observations in the 0–20 cm soil depth. Soil chemical and physical variables are shown by black continuous arrows, microbial processes and soil CO<sub>2</sub> emissions are shown by grey heavy dotted arrows, microbial <span class="html-italic">H’</span> is shown by black heavy dotted arrows, and microbial abundance is shown by light dotted arrows. Observations are represented by black stars (plot 1), white triangles (plot 2), and white squares (plot 3). PC 1 and PC 2 axes together accounted for 77.78% of the variability and were significant (<span class="html-italic">p</span> &lt; 0.05) (<b>a</b>). PCA biplot was based on soil chemical parameters, microbial abundance, and <span class="html-italic">H’</span>, which were used as variables, while soil depths were considered as observations. Observations (10: 0–10 cm, 20:10–20 cm, 40: 20–40 cm, 60: 40–60 cm, 80: 60–80 cm, 100: 80–100 cm) are represented by black circles. In plot 1, the PC 1 and PC 2 axes together accounted for 76.04% of the variability and were significant (<span class="html-italic">p</span> &lt; 0.05) (<b>b</b>); in plot 2, the PC 1 and PC 2 axes together accounted for 81.22% of the variability and were significant (<span class="html-italic">p</span> &lt; 0.05) (<b>c</b>); in plot 3, the PC 1 and PC 2 axes together accounted for 82.84% of the variability and were significant (<span class="html-italic">p</span> &lt; 0.05) (<b>d</b>).</p>
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14 pages, 570 KiB  
Article
Understanding the Impact of Soil Characteristics and Field Management Strategies on the Degradation of a Sprayable, Biodegradable Polymeric Mulch
by Cuyler K Borrowman, Raju Adhikari, Kei Saito, Stuart Gordon and Antonio F. Patti
Agriculture 2024, 14(11), 2062; https://doi.org/10.3390/agriculture14112062 (registering DOI) - 15 Nov 2024
Viewed by 176
Abstract
The use of non-degradable plastic mulch has become an essential agricultural practice for increasing crop yields, but continued use has led to contamination problems and in some cropping areas decreases in agricultural productivity. The subsequent emergence of biodegradable plastic mulches is a technological [...] Read more.
The use of non-degradable plastic mulch has become an essential agricultural practice for increasing crop yields, but continued use has led to contamination problems and in some cropping areas decreases in agricultural productivity. The subsequent emergence of biodegradable plastic mulches is a technological solution to these issues, so it is important to understand how different soil characteristics and field management strategies will affect the rate at which these new materials degrade in nature. In this work, a series of lab-scale hydrolytic degradation experiments were conducted to determine how different soil characteristics (type, pH, microbial community composition, and particle size) affected the degradation rate of a sprayable polyester–urethane–urea (PEUU) developed as a biodegradable mulch. The laboratory experiments were coupled with long-term, outdoor, soil degradation studies, carried out in Clayton, Victoria, to build a picture of important factors that can control the rate of PEUU degradation. It was found that temperature and acidity were the most important factors, with increasing temperature and decreasing pH leading to faster degradation. Other important factors affecting the rate of degradation were the composition of the soil microbial community, the mass loading of PEUU on soil, and the degree to which the PEUU was in contact with the soil. Full article
(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)
25 pages, 978 KiB  
Review
The Intriguing Connection Between the Gut and Lung Microbiomes
by Magdalena Druszczynska, Beata Sadowska, Jakub Kulesza, Nikodem Gąsienica-Gliwa, Ewelina Kulesza and Marek Fol
Pathogens 2024, 13(11), 1005; https://doi.org/10.3390/pathogens13111005 - 15 Nov 2024
Viewed by 189
Abstract
Recent advances in microbiome research have uncovered a dynamic and complex connection between the gut and lungs, known as the gut–lung axis. This bidirectional communication network plays a critical role in modulating immune responses and maintaining respiratory health. Mediated by immune interactions, metabolic [...] Read more.
Recent advances in microbiome research have uncovered a dynamic and complex connection between the gut and lungs, known as the gut–lung axis. This bidirectional communication network plays a critical role in modulating immune responses and maintaining respiratory health. Mediated by immune interactions, metabolic byproducts, and microbial communities in both organs, this axis demonstrates how gut-derived signals, such as metabolites and immune modulators, can reach the lung tissue via systemic circulation, influencing respiratory function and disease susceptibility. To explore the implications of this connection, we conducted a systematic review of studies published between 2001 and 2024 (with as much as nearly 60% covering the period 2020–2024), using keywords such as “gut–lung axis”, “microbiome”, “respiratory disease”, and “immune signaling”. Studies were selected based on their relevance to gut–lung communication mechanisms, the impact of dysbiosis, and the role of the gut microbiota in respiratory diseases. This review provides a comprehensive overview of the gut–lung microbiome axis, emphasizing its importance in regulating inflammatory and immune responses linked to respiratory health. Understanding this intricate pathway opens new avenues for microbiota-targeted therapeutic strategies, which could offer promising interventions for respiratory diseases like asthma, chronic obstructive pulmonary disease, and even infections. The insights gained through this research underscore the potential of the gut–lung axis as a novel target for preventative and therapeutic approaches in respiratory medicine, with implications for enhancing both gut and lung health. Full article
18 pages, 3561 KiB  
Article
The Effects of Low Concentrations and Long-Term Contamination by Sodium Dodecyl Sulfate on the Structure and Function of Bacterial Communities in the Lake–Terrestrial Ecotone
by Lingquan Zeng, Qi Zhu, Chunhua Li and Chun Ye
Microorganisms 2024, 12(11), 2330; https://doi.org/10.3390/microorganisms12112330 - 15 Nov 2024
Viewed by 245
Abstract
Due to the growing focus on daily hygiene practices, sodium dodecyl sulfate (SDS), a widely used surfactant, is increasingly found in domestic sewage and rainfall runoff. Upon entering the lake–terrestrial ecotone, SDS affects the composition, abundance, and functional capacity of soil bacterial communities [...] Read more.
Due to the growing focus on daily hygiene practices, sodium dodecyl sulfate (SDS), a widely used surfactant, is increasingly found in domestic sewage and rainfall runoff. Upon entering the lake–terrestrial ecotone, SDS affects the composition, abundance, and functional capacity of soil bacterial communities due to its bacteriostatic properties. To investigate the effects of long-term discharge of sewage containing low concentrations of SDS on microorganisms in the lake–terrestrial ecotone, alterations in bacterial community structure, functional genes, and biomass were examined using a simulated continuous pollutant input. The results indicated the following: (1) The degradation rate of sodium dodecyl sulfate (SDS) by soil microorganisms in the lake–terrestrial ecotone under long-term and low concentrations of SDS stress ranged from 11 to 16 mg/kg·d. (2) The effects of low concentrations and long-term SDS stress on bacterial community structure and gene function in the lake–terrestrial ecotone differed significantly from those of short-term pollution. The damage to microbial-promoted material cycling in the lake–terrestrial ecotone was more severe; however, the proliferation of pathogenic bacteria remained continuously suppressed. (3) Soil bacteria in the lake–terrestrial ecotone responded to the stress of long-term and low concentrations of SDS primarily by enhancing chemotaxis and tolerance. Full article
(This article belongs to the Special Issue Microbial Communities in Aquatic Environments)
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<p>Image of sodium dodecyl sulfate (SDS) input to lake–terrestrial ecotones, experimental and indoor simulation device.</p>
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<p>Image of sodium dodecyl sulfate (SDS) input to lake–terrestrial ecotones, experimental and indoor simulation device.</p>
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<p>SDS degradation rate in the analyzed experimental variants during the 10 days of the experiment.</p>
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<p>Histograms illustrating the changes in Chao (<b>a</b>) and Shannon (<b>b</b>) indices, along with a PCoA analysis plot (<b>c</b>), across various treatments during the 10-day experimental period.</p>
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<p>Histograms illustrating the changes in soil microbial biomass carbon and nitrogen content across various treatments during the 10-day experimental period.</p>
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<p>Collinearity diagram of the distribution of the main phylum of the lake–terrestrial ecotone bacterial community in the simulation experiment.</p>
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<p>Distribution histogram of the main families of soil microorganisms in the lake–terrestrial ecotone.</p>
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<p>Distribution histogram of the main genera of soil microorganisms in the lake–terrestrial ecotone.</p>
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<p>Heat map of KO metabolic pathways in soil microorganisms under long-term and low-concentration SDS input condition.</p>
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23 pages, 4764 KiB  
Article
Sex-Induced Changes in Microbial Eukaryotes and Prokaryotes in Gastrointestinal Tract of Simmental Cattle
by Diórman Rojas, Richard Estrada, Yolanda Romero, Deyanira Figueroa, Carlos Quilcate, Jorge J. Ganoza-Roncal, Jorge L. Maicelo, Pedro Coila, Wigoberto Alvarado and Ilse S. Cayo-Colca
Biology 2024, 13(11), 932; https://doi.org/10.3390/biology13110932 - 15 Nov 2024
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Abstract
This study investigates gender-based differences in the gut microbiota of Simmental cattle, focusing on bacterial, archaeal, and fungal communities. Fecal samples were collected and analyzed using high-throughput sequencing, with taxonomic classification performed through the SILVA and UNITE databases. Alpha and beta diversity metrics [...] Read more.
This study investigates gender-based differences in the gut microbiota of Simmental cattle, focusing on bacterial, archaeal, and fungal communities. Fecal samples were collected and analyzed using high-throughput sequencing, with taxonomic classification performed through the SILVA and UNITE databases. Alpha and beta diversity metrics were assessed, revealing significant differences in the diversity and composition of archaeal communities between males and females. Notably, females exhibited higher alpha diversity in archaea, while beta diversity analyses indicated distinct clustering of bacterial and archaeal communities by gender. The study also identified correlations between specific microbial taxa and hematological parameters, with Treponema and Methanosphaera showing gender-specific associations that may influence cattle health and productivity. These findings highlight the importance of considering gender in microbiota-related research and suggest that gender-specific management strategies could optimize livestock performance. Future research should explore the role of sex hormones in shaping these microbial differences. Full article
(This article belongs to the Special Issue Structure, Function and Diversity of Gut Microbes in Animals)
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<p>Comparison of alpha diversity of archaea in the cattle gut microbiome between females and males. Y = year, S = sex. YxS = Year x Sex (<b>A</b>) Observed. (<b>B</b>) ACE. (<b>C</b>) Fisher. (<b>D</b>) PD. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>PCoA plots of beta diversity in cattle gut microbiota based on sex. (<b>A</b>) Bacteria (Jaccard distance). (<b>B</b>) Fungi (Jaccard distance). (<b>C</b>) Fungi (unweighted Unifrac). (<b>D</b>) Archaea (unweighted Unifrac).</p>
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<p>Relative abundance of microbial phyla in the gut microbiota of cattle by sex. (<b>A</b>) Bacteria. (<b>B</b>) Fungi. (<b>C</b>) Archaea.</p>
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<p>Heatmaps of the relative abundance of microbial genera in the gut microbiota of cattle by sex. (<b>A</b>) Bacteria. (<b>B</b>) Fungi. (<b>C</b>) Archaea.</p>
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<p>Spearman correlation between gut microbiota and hematological parameters in female and male cattle. (<b>A</b>) Bacteria. (<b>B</b>) Fungi. (<b>C</b>) Archaea. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Differential abundance analysis of the bovine gut microbiota by sex. (<b>A</b>) Bacterial taxa. (<b>B</b>) Fungal taxa. (<b>C</b>) Archaeal taxa. Log2 fold changes indicate significant enrichment (<span class="html-italic">p</span> &lt; 0.05) in male (brown) or female (black) groups.</p>
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<p>Spearman correlations between alpha diversity indices and hematological parameters in cattle. (<b>A</b>) Fungi. (<b>B</b>) Archaea.</p>
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16 pages, 4758 KiB  
Article
Soil Microbial and Metabolomic Shifts Induced by Phosphate-Solubilizing Bacterial Inoculation in Torreya grandis Seedlings
by Yi Li, Yuanyuan Guan, Zhengchu Jiang, Qiandan Xie, Qi Wang, Chenliang Yu and Weiwu Yu
Plants 2024, 13(22), 3209; https://doi.org/10.3390/plants13223209 - 15 Nov 2024
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Abstract
Phosphorus is crucial for plant growth and development, but excess fertilizer not absorbed by plants often binds with metal ions like iron and manganese, forming insoluble compounds that contribute to soil environmental pollution. This study investigates the impact of Burkholderia sp., a phosphate-solubilizing [...] Read more.
Phosphorus is crucial for plant growth and development, but excess fertilizer not absorbed by plants often binds with metal ions like iron and manganese, forming insoluble compounds that contribute to soil environmental pollution. This study investigates the impact of Burkholderia sp., a phosphate-solubilizing bacterium utilized as a biofertilizer, on the fertility of T. grandis soil, alongside the associated shifts in soil metabolites and their relationship with microbial communities after inoculation. The soil microbial community structures and metabolite profiles were analyzed via amplicon sequencing and high-resolution untargeted metabolomics. The inoculation of phosphate-solubilizing bacteria led to a significant (p < 0.05) enhancement in total phosphorus, potassium, and nitrogen concentrations in the soil, with a marked increase in available phosphorus in bulk soil (p < 0.05). Moreover, the microbial community structure exhibited significant shifts, particularly in the abundance of bacterial phyla such as Acidobacteria, Chloroflexi, Proteobacteria, and the fungal phylum Ascomycota. Metabolomic analysis revealed distinct metabolites, including fatty acids, hormones, amino acids, and drug-related compounds. Key microbial taxa such as Chloroflexi, Proteobacteria, Acidobacteria, Verrucomicrobia, Mucoromycota, and Ascomycota indirectly contributed to soil phosphorus metabolism by influencing these differential metabolites. In conclusion, the application of phosphate-solubilizing bacteria offers an innovative approach to improving soil quality in T. grandis, promoting phosphorus utilization efficiency, and enhancing soil ecosystem health by optimizing microbial communities and metabolite compositions. Full article
(This article belongs to the Special Issue Nutrient Management on Soil Microbiome Dynamics and Plant Health)
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<p>Soil physicochemical properties across the various groups, with statistical significance denoted as * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, and *** <span class="html-italic">p</span> ≤ 0.001, **** <span class="html-italic">p</span> ≤ 0.0001. “W” signifies the bacterial agent treatment group, “CK” refers to the control group, and “RS” and “BS” represent rhizosphere soil and bulk soil, respectively.</p>
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<p>Changes in microbial diversity following treatment with phosphate-solubilizing bacteria in <span class="html-italic">T. grandis</span>. Panels (<b>a</b>,<b>b</b>) show the composition of bacterial and fungal communities, respectively, while (<b>c</b>,<b>d</b>) display the alpha diversity of these communities. Panels (<b>e</b>,<b>f</b>) present the principal component analysis (PCA) plots for bacteria and fungi, respectively. In Figures (<b>a</b>,<b>b</b>), the horizontal axis represents the four subgroups (CKBS, CKRS, WBS, WRS), and the vertical axis reflects the abundance of species at the genus level. The length of the bars corresponds to species abundance. The box plots in Figures (<b>c</b>,<b>d</b>) depict Chao1, which indicates total species richness, while the Simpson and Shannon indices measure microbial diversity within the soil samples. Good’s coverage reflects sample coverage. The <span class="html-italic">p</span>-value atop the box plot indicates the statistical significance of diversity index differences among the subgroups.</p>
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<p>Species-level changes in soil microorganisms across different groups. (<b>a</b>) Bacterial changes in the bulk soil. (<b>b</b>) Fungal changes in the bulk soil. (<b>c</b>) Bacterial changes in the rhizosphere soil. (<b>d</b>) Fungal changes in the rhizosphere soil. The horizontal axis represents the ASVs, organized according to their taxonomic information from phylum to species, while the vertical axis reflects the −log10(adj-Pvalue) values. Each dot or circle represents an ASV, with its size indicating relative abundance, expressed in log2(CPM/n). The dotted line represents significance analysis. Microorganisms below the dotted line are not significant. The symbols above the dotted line are significant. A grayscale background highlights the top 10 genera with the most significantly upregulated points (default setting), and the genus name is labeled at the top of the figure.</p>
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<p>Microbial functional prediction analysis. Functional predictions for bacterial communities in bulk (<b>a</b>) and rhizosphere (<b>b</b>) soils, and fungal communities in bulk (<b>c</b>) and rhizosphere (<b>d</b>) soils. The vertical axis reflects the degree of upregulation (positive) or downregulation (negative) following inoculation. The colored dots indicate the significance of pathway changes (represented as the −log10 of the <span class="html-italic">p</span>-value), while the size of the dots corresponds to the extent of fold changes (represented as the log2 of the fold change).</p>
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<p>Analysis of significantly differential metabolites in soil. (<b>a</b>) Total metabolites identified across all soil samples. PCA of metabolites in positive-ion mode (POS) (<b>b</b>) and negative-ion mode (NEG). (<b>c</b>) The clustering of three samples in the same group indicates good reproducibility. A heatmap displaying significant differences in soil metabolites for POS (<b>d</b>) and NEG (<b>e</b>). The vertical axis lists the significantly different metabolites, while the horizontal axis shows the different groups. Yellow indicates elevated expression levels of significantly different metabolites, and blue indicates reduced expression levels. WBS and WRS represent treatment groups, while CKBS and CKRS serve as control groups.</p>
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<p>Correlation analysis of bacterial and fungal phyla with significantly differential metabolites.The asterisks (*, **, ***) represent the level of significance, where * indicates <span class="html-italic">p</span> ≤ 0.05, ** indicates <span class="html-italic">p</span> ≤ 0.01, and *** indicates <span class="html-italic">p</span> ≤ 0.001. (<b>a</b>) Correlation between bacterial phyla and significantly differential metabolites in bulk soil. (<b>b</b>) Correlation between fungal phyla and significantly differential metabolites in bulk soil. (<b>c</b>) Correlation between bacterial phyla and significantly differential metabolites in rhizosphere soil. (<b>d</b>) Correlation between fungal phyla and significantly differential metabolites in rhizosphere soil. Horizontal axis displays significantly differential metabolites (with some shown as abbreviations), while vertical axis represents microorganisms (ten most abundant phyla). Red indicates positive correlation, and green indicates negative correlation. 1,2-Dipalmitoyl-sn-glycero-3-phospho(1′-rac-glycerol) (DPPG).</p>
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<p>Schematic diagram of pot experiment.</p>
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13 pages, 4063 KiB  
Article
Community Structure and Biodiversity of Active Microbes in the Deep South China Sea
by Taoran Yang, Yinghui He, Ming Yang, Zhaoming Gao, Jin Zhou and Yong Wang
Microorganisms 2024, 12(11), 2325; https://doi.org/10.3390/microorganisms12112325 - 15 Nov 2024
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Abstract
The deep ocean harbors a group of highly diversified microbes, while our understanding of the active microbes that are real contributors to the nutrient cycle remains limited. In this study, we report eukaryotic and prokaryotic communities in ~590 m and 1130 m depths [...] Read more.
The deep ocean harbors a group of highly diversified microbes, while our understanding of the active microbes that are real contributors to the nutrient cycle remains limited. In this study, we report eukaryotic and prokaryotic communities in ~590 m and 1130 m depths using 16S and 18S rRNA Illumina reads (miTags) extracted from 15 metagenomes (MG) and 14 metatranscriptomes (MT). The metagenomic 16S miTags revealed the dominance of Gammaproteobacteria, Alphaproteobacteria, and Nitrososphaeria, while the metatranscriptomic 16S miTags were highly occupied by Gammaproteobacteria, Acidimicrobiia, and SAR324. The consistency of the active taxa between the two depths suggests the homogeneity of the functional microbial groups across the two depths. The eukaryotic microbial communities revealed by the 18S miTags of the metagenomic data are dominated by Polycystinea; however, they were almost all absent in the 18S metatranscriptomic miTags. The active eukaryotes were represented by the Arthropoda class (at 590 m depth), Dinophyceae, and Ciliophora classes. Consistent eukaryotic communities were also exhibited by the 18S miTags of the metatranscriptomic data of the two depths. In terms of biodiversity, the ACE and Shannon indices of the 590 m depth calculated using the 18S metatranscriptomic miTags were much higher than those of the 1130 m depth, while a reverse trend was shown for the indices based on the metagenomic data. Our study reports the active microbiomes functioning in the nutrient utilization and carbon cycle in the deep-sea zone, casting light on the quantification of the ecological processes occurring in the deep ocean. Full article
(This article belongs to the Section Environmental Microbiology)
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<p>Sampling sites and method. (<b>A</b>) Three deep-sea lander deployments with the MISNAC apparatus obtained a total of 15 samples at depths of approximately 590 m and 1130 m. (<b>B</b>) A deep-sea lander named ‘Phoenix’ equipped with (<b>C</b>) sampling device MISNAC. (<b>D</b>) The sampling sites of the cruise in the South China Sea.</p>
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<p>The physicochemical analyses were measured for the plots.</p>
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<p>Relative abundance and diversity indices of the abundant and active prokaryotes based on the 16S miTags extracted from metagenomes (MG) and metatranscriptomes (MT). (<b>A</b>) The structures of the prokaryotic communities at the class level in the samples gathered at different depths. Minor groups contain all the small taxa with relative abundance &lt;5%. (<b>B</b>) Venn plot showing the shared prokaryotic OTUs among the different depths. (<b>C</b>) Principal coordinates analysis (PCoA) plot using the prokaryotic community structures.</p>
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<p>Procrustes analysis connecting the metagenome communities and metatranscriptome communities in the 590 m and 1130 m samples of (<b>A</b>) prokaryotes and (<b>B</b>) eukaryotes.</p>
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<p>Relative abundance and diversity indices of the abundant and active eukaryotes based on the 18S miTags extracted from metagenomes (MG) and metatranscriptomes (MT). (<b>A</b>) The structures of the eukaryotic communities at the class level in the samples gathered at different depths. Minor groups contain all the small taxa with relative abundance &lt;5%. (<b>B</b>) Venn plot showing the shared eukaryotic OTUs among the different depths. (<b>C</b>) Principal coordinates analysis (PCoA) plot using the eukaryotic community structures.</p>
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<p>Alpha diversity and UPGMA tree of the abundant and active prokaryotes based on the 16S miTags. (<b>A</b>) Alpha diversity indices for the prokaryotic communities extracted from metagenomes (MG). (<b>B</b>) Alpha diversity indices for the prokaryotic communities extracted from metatranscriptomes (MT). (<b>C</b>) UPGMA tree generated from the Bray–Curtis dissimilarity matrix of the abundant and active prokaryotes for the different depths. The IDs with ‘MG’ refer to the communities derived from the 16S metagenomic miTags; those with ‘MT’ denote those derived from the 16S metatranscriptomic miTags.</p>
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<p>Alpha diversity and UPGMA tree of the abundant and active eukaryotes based on the 18S miTags. (<b>A</b>) Alpha diversity indices for the eukaryotic communities extracted from metagenomes (MG). (<b>B</b>) Alpha diversity indices for the eukaryotic communities extracted from metatranscriptomes (MT). (<b>C</b>) UPGMA tree generated from the Bray–Curtis dissimilarity matrix of the abundant and active eukaryotes for the different depths. The IDs with ‘MG’ refer to the communities derived from the metagenomic 16S miTags; those with ‘MT’ denote those derived from the 16S metatranscriptomic miTags.</p>
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